The Good, the Bad, and the Vision: Exploring the Mental Health Care Experiences of Transitional-Aged Youth Using the Photovoice Method
Bibliographic record
Abstract
Transitional-aged youth (TAY) between the ages of 16 and 24 experience higher rates of mental distress than any other age group. It has long been recognized that stability, consistency, and continuity in mental health care delivery are of paramount importance; however, the disjointed progression from paediatric to adult psychiatric services leaves many TAY vulnerable to deleterious health outcomes. In Spring 2019, eight TAY living with mental health challenges participated in a Photovoice study designed to: (1) illuminate their individual transition experiences; and, (2) support a collective vision for optimal mental health care at this nexus. Participants took photographs that reflected three weekly topics— the good, the bad, and the vision—and engaged in a series of three corresponding photo-elicitation focus group sessions. Twenty-four images with accompanying titles and captions were sorted into nine participant-selected themes. Findings contribute to an enhanced awareness of psychiatric service delivery gaps experienced by TAY, and advocate for seamless and supportive transitions that more effectively meet the mental health care needs of this population.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".